Target Tracking Formulation of the SVSF With Data Association Techniques

Attari, Mina; Habibi, Saeid; Gadsden, S. Andrew

doi:10.1109/taes.2017.2649138

Cited by 21 publications

(15 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To sum up, the ISVSF has good ness and accuracy. In the simulation, the SVSF with Luenberger's strategy (SVSF-L) [24,26] and the SVSF with "artificial" velocity measurements (SVSF-V) [17,21] are applied to estimate velocity. The SVSF and UK-SVSF have large estimation errors when the target maneuvers, so they are not shown in the simulations.…”

Section: A Comprehensive Simulationmentioning

confidence: 99%

“…Additionally, the second-order SVSF [23] and other methods [20,24] have been proposed to improve the stability and robustness. Since its development, the SVSF has been applied in various applications [25], such as vehicle navigation [24,[26][27][28][29][30][31], fault detection and diagnosis [32,33], battery management [34][35][36][37], and artificial intelligence [33,38,39]. However, it was found that the SVSF can be further improved if two shortcomings can be solved.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Smooth Variable Structure Filter for Robust Target Tracking

Chen

Wang

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation performance is usually insufficient in real cases where the measurement vector is of fewer dimensions than the state vector, an improved SVSF (ISVSF) is proposed by combining the existing SVSF with Bayesian theory. The ISVSF contains two steps: firstly, a preliminary estimation is performed by SVSF. Secondly, Bayesian formulas are adopted to improve the estimation for higher accuracy. The ISVSF shows high robustness in dealing with modeling uncertainties and noise. It is noticeable that ISVSF could deliver satisfying performance even if the state of the system is undergoing a sudden change. According to the simulation results of target tracking, the proposed ISVSF performance can be better than that obtained with existing filters.

show abstract

Section: A Comprehensive Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Smooth Variable Structure Filter for Robust Target Tracking

Chen

Wang

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Some of the early improvements to the SVSF include a covariance derivation 5,6 , an optimal time varying smoothing boundary width 3,4 , and a strategy for better dealing with missing measurements 4 . In addition, the SVSF has been integrated with Interacting Multiple Model adaptive strategies 5 , a second order SVSF formulation has been derived 7,8 , as has seen many other improvements and applications 9,10,11,12,13,14,15,16 .…”

Section: Introductionmentioning

confidence: 99%

A multiple model adaptive SVSF-KF estimation strategy

Goodman

Wilkerson

Eggleton

et al. 2019

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

Self Cite

View full text Add to dashboard Cite

State estimation strategies play a critical role in obtaining accurate information about the state of dynamic systems as they develop. Such information can be important on its own and critical for precise and predictable control of such systems. The Kalman filter (KF) is a classic algorithm and among the most powerful tools in state estimation. The Kalman filter however can be sensitive to modeling uncertainty and sudden changes in system dynamics. The Smooth Variable Structure Filter (SVSF) is a relatively new estimation strategy that operates on variable structure concepts. In general, the SVSF has the advantage that is can be quite robust to modeling uncertainty and sudden fault conditions. Recent advancements to the SVSF, such as the addition of a covariance formulation, and the derivation of a time varying smoothing boundary layer (VBL), have allowed for combined SVSF-KF strategies. In a typical SVSF-KF approach, the VBL is used to detect the presence of a system fault, and switch from the more optimal KF gain to the more robust SVSF gain. While this approach has been proven effective in several cases, there are circumstances where the VBL will fail to indicate the presence of an ongoing fault. A new form of the SVSF-KF is proposed, based on the framework of the Multiple Model Adaptive Estimator.

show abstract

“…The Kalman filter [1, 2] is an effective tool to deal with state estimation and has successful applications in many fields [3], e.g. orbit determination [4], target tracking [5, 6], statistical signal processing [7], information fusion [8], weather forecasting [9], navigation [10], and robotics [11]. Under the Gaussian assumption for the initial state and all noises entering into the system, the Kalman filter is the optimal minimum mean squared error (MSE) state estimator.…”

Section: Introductionmentioning

confidence: 99%